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Open AccessArticle

Super-Resolution of Thermal Images Using an Automatic Total Variation Based Method

1
Department of Mathematics, University of Bologna, 40127 Bologna, Italy
2
Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, 40136 Bologna, Italy
3
Department of Computer Science and Engineering, University of Bologna, 40127 Bologna, Italy
*
Author to whom correspondence should be addressed.
Current address: DICAM, viale del Risorgimento 2, 40136 Bologna, Italy.
These authors contributed equally to this work.
Remote Sens. 2020, 12(10), 1642; https://doi.org/10.3390/rs12101642
Received: 23 April 2020 / Revised: 15 May 2020 / Accepted: 18 May 2020 / Published: 20 May 2020
The relatively poor spatial resolution of thermal images is a limitation for many thermal remote sensing applications. A possible solution to mitigate this problem is super-resolution, which should preserve the radiometric content of the original data and should be applied to both the cases where a single image or multiple images of the target surface are available. In this perspective, we propose a new super-resolution algorithm, which can handle either single or multiple images. It is based on a total variation regularization approach and implements a fully automated choice of all the parameters, without any training dataset nor a priori information. Through simulations, the accuracy of the generated super-resolution images was assessed, in terms of both global statistical indicators and analysis of temperature errors at hot and cold spots. The algorithm was tested and applied to aerial and terrestrial thermal images. Results and comparisons with state-of-the-art methods confirmed an excellent compromise between the quality of the high-resolution images obtained and the required computational time.
Keywords: super-resolution; thermal images; regularized reconstruction; total variation regularization; automatic regularization super-resolution; thermal images; regularized reconstruction; total variation regularization; automatic regularization
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MDPI and ACS Style

Cascarano, P.; Corsini, F.; Gandolfi, S.; Piccolomini, E.L.; Mandanici, E.; Tavasci, L.; Zama, F. Super-Resolution of Thermal Images Using an Automatic Total Variation Based Method. Remote Sens. 2020, 12, 1642.

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